1. The Cancer Genome Browser Sofie Salama COAT-PhD Summer School 2012 1

2. The Cancer Genome Browser OUTLINE –Slide show to introduce the Cancer Genomics Browser What’s there? How to visualize the data? Tools –Live Demo Basic setup Breast cancer data –Using signatures –Microarray vs RNA-Seq –Comparing across datasets GBM data –Genesets –What genes correlate with phenotypes? –Playtime! 2

3. 3 https://genome.ucsc.edu UCSC Genome Browser Base level to full genome display capability ENCODE Human sequence variation Whole genome association studies Human genetic and disease related genome annotation

4. 4 https://genome-cancer.ucsc.edu Large-scale Medical Genomics Datasets New issues arise to visualize high-throughput cancer genomics data: data security and access control, sample cohort, multi-analytes, and clinical and phenotypic information.

5. 5 UCSC Cancer Genomics Browser Simultaneously display patient genomic and clinical data from a cohort of samples Base level to full genome display capability Multiple studies Growing list of published studies, including public-tier TCGA data Integrated with popular UCSC Genome Browser and its vast store of genomic information Zhu J et. al Nature Methods. 2009 Sanborn JZ et.al. Nucleic Acids Res. 2010

6. New UCSC Cancer Browser Portal genome-cancer.ucsc.edu

7. User Interface: A portal to display high throughput data sets Teresa Swatloski, Brian Craft, Mary Goldman genome-cancer.ucsc.edu

8. toggle on/off RefSeq genes link to tumor image browser link to human genome browser user sign in help menu view in chromosome mode select dataset to view configure genesets configure genomic signatures view in gene mode resize panels position or gene search bar User Interface Features Teresa Swatloski, Brian Craft, Mary Goldman genome-cancer.ucsc.edu

9. Dataset selection showing TCGA breast cancer data TCGA breast cancer datasets Gene expression, copy number, DNA Methylation, RPPA, Paradigmlite TCGA clinical data Teresa Swatloski

10. Genomic and phenotypic data heatmaps Genomic dataClinical data genome-cancer.ucsc.edu

11. Individual dataset layout Samples Genomic dataClinical data Genomic locations / Genes genome-cancer.ucsc.edu

12. Samples Clinical Heatmap sample_typedays_to_last_followup Solid tissue normal Primary solid tumor amplificationdeletion Genomics Heatmap Metastatic Multiple clinical features Clinical data encoded in color

13. Sample sorting determined by clinical data Sample (i.e. vertical) order is determined by the clinical data on the right The samples is always sorted by clinical features Tie break using subsequent clinical features Samples genome-cancer.ucsc.edu

14. Zoom in to See Individual Sample drag zoom slider genome-cancer.ucsc.edu

15. genomic heatmap clinical heatmap heatmap view adjust display coloring configuration window for clinical variables, sample subgrouping and statistics box plot summar y view proportions summary view click to show dataset detail remove dataset Individual Dataset Control Teresa Swatloski

16. Summary Views Heatmap View - Amplified / Deleted Regions Proportions Summary View Box Plot Summary View

17. glioblastoma multiforme breast carcinoma lung squamous cell DNA Copy Number Profile Summary View TCGA CNV

18. glioblastoma multiforme breast carcinoma lung squamous cell DNA Copy Number Profile Summary View TCGA CNV EGFR CDKN2A, CDKN2B

19. Genes View Mode genome-cancer.ucsc.edu

20. 20 “Genes” Configuration Currently displayed gene list Three ways to add a gene list Type or copy and paste user defined genes 1 2 3 genome-cancer.ucsc.edu Teresa Swatloski

21. Genes view to see the PAM50 intrinsic gene expression subtypes in TCGA Breast data Basal LuA LuB Her2-like Normal-like PAM50: Parker et al., Journal of Clinical Oncology (2009)

22. Basal LumA LumB Her2 Tumor Solid normal Same thing with RNA-Seq Data

23. Online statistical tests compare two subgroups Samples Subgroup samples genome-cancer.ucsc.edu

24. Online statistical tests compare two subgroups Samples Subgroup samples p values genome-cancer.ucsc.edu

25. click to view detail and use the variable to subgroup samples perform statistical tests to compare subgroup1 and subgroup2 subgroup 1 subgroup 2 variables used in defining subgroups “Active Feature List” area Sample subgroup configuration

26. Compare subgroups using the summary view EGFR amplification in GBM is largely in the non CpG island DNA methylator samples (non G-CIMP) methylator samples in GBM is largely proneural by gene expression, also from younger patients, with better survival

27. Evaluate Genomic Signature on the Browser B. Computed signatures online -> approximate prediction A. Enter signature as an algebraic expression

28. Evaluate Genomic Signature on the Browser 21 gene signature predicts rate of recurrence at 10 yr in ER+ patients treated with TAM (Paik 2004) Genomic signature online approximation: higher score -> higher likelihood of recurrence; low score -> lower likelihood of recurrence

29. Evaluate Genomic Signature on the Browser Browser view of ER+ patients in a preoperative chemotherapy study dataset Signature score correlates with pathCR: the paradox that ER+ patient who is more likely to have recurrent disease in 10 years treated with TAM is also more likely to respond to chemotherapy

30. Genomic Signature Configuration Current signatures Three ways to add a genomic signature 1 2 3 Enter signature as an algebraic expression Such as: + TP53 – 0.25* ERBB2 Teresa Swatloski

31. User Support genome-cancer@soe.ucsc.edu 31 Mary Goldman, Teresa Swatloski

32. Web API Create a url to specify a view to the cancer browser base: https://genome-cancer.ucsc.edu/hgHeatmap/#?https://genome-cancer.ucsc.edu/hgHeatmap/#? data track(s): comma separated gene names display mode gene list: coma separated gene names chromosomal position genomic signature : e.g. +TP53-0.25*ERBB2 Examples dataset=vijver2002&pos=chr2:123767566-chr2:187943340 dataset=ucsfNeveCGH&displayas=geneset&gene_list=TP53,ERBB2 Documentation https://genome-cancer.soe.ucsc.edu/proj/site/help Brian Craft, Mary Goldman

33. User Account and Security Brian Craft genome-cancer.ucsc.edu

34. cgData: Cancer Genomic data specification Gene expression, copy number, RPPA, DNA methlylation, siRNA viability, phenotypes, clinical data Support large-scale genomic data repository - Currently supports Cancer Browser - Plan to support automated data analysis pipeline “Solve” (address) common data linking problem Meta data tracking Once data in this specification, automated data ingestion to UCSC Cancer Browser Kyle Ellrott

35. Cancer Browser Updates Current improved version launched January, 2012 Monthly data freeze Latest freeze data viewable on the Cancer Browser within a few days July, 2012 – Added ability to download processed datasets and improved user interface for clinical features, subgrouping and statistics

36. Data freeze 2012-02-28 summary (sample number)

37. 37 Summary Simultaneously display patient genomic and clinical data from a cohort of samples Multiple studies data visualization Base level to full genome, and genesets display capability cgData data repository driven Monthly data freeze and version control User account Project-specific access-control Single signon portal Provide web API for linking genome-cancer@soe.ucsc.edu

38. DCC, Firehose UCSC cgData Repository UCSC Next-gen Sequencing Data Analysis DNA-seq (bambam, bridget) DNA-seq (bambam, bridget) mutation, allelic-specific copy number, structural rearrangement mutation, allelic-specific copy number, structural rearrangement Combined RNA/DNA analysis Combined RNA/DNA analysis RNA editing RNA editing converter browser pathway analysis Clinical Predictors (TopModel) Bam files Mutation call comparison PARADIGM pathway analysis UCSC Cancer Genomics Browser cBio

39. 39 UCSC Cancer Genomics Group  Brian Craft  Teresa Swatloski  Mary Goldman  Kyle Ellrott  Erich Weiler  Chris Wilks  Singer Ma  Christopher Szeto  Sofie Salama  Mia Grifford  Sam Ng  Ted Goldstein  Dan Carlin  Daniel Zerbino  Melissa Cline  Mark Diekhans  Josh Stuart  David Haussler Collaborators  The Cancer Genome Atlas  Stand Up To Cancer  Intl. Cancer Genomics Consortium  ISPY consortium  MSKCC  LINCS consortium  Christopher Benz, Buck Institute  Laura Esserman, UCSF  Joe Gray, OHSU  Eric Collisson, UCSF  Gordon Mills, MDACC  Rachel Schiff, BCM Funding Agencies  NCI/NIH, NHGRI  American Association for Cancer Research Acknowledgment

40. The Cancer Genome Browser OUTLINE –Slide show to introduce the Cancer Genomics Browser What’s there? How to visualize the data? Tools –Live Demo Basic setup Breast cancer data –Using signatures –Microarray vs RNA-Seq –Comparing across datasets GBM data –Genesets –What genes correlate with phenotypes? –Playtime! 40

41. cgData Packages genomic data A (CNV) genomic data B (RPPA) clinical data1 ( FFPE, timepoint ) clinical data 2 ( patient, age,.. ) meta-data Most likely your data files Need to add meta data file meta-data

42. cgData Packages idMap (TCGA BRCA) genomic data A (CNV) genomic data B (RPPA) clinical data1 ( FFPE, timepoint ) clinical data 2 ( patient, age,.. ) TCGA-01-ABCD-01A TCGA-01-ABCD-01A-EG TCGA-01-ABCD TCGA-01-ABCD-01A-JH patient sample aliquot sample aliquot

43. cgData Packages idMap (TCGA BRCA) genomic data A (CNV) genomic data B (RPPA) clinical data1 ( FFPE, timepoint ) clinical data 2 ( patient, age,.. ) Mostly likely already in UCSC cgData library Most likely your data files Need to add meta data file Identifiers used in data files parent-child relationships probeMap B assembly (hg18) probeMap B (antibody)